# dependencies
library(tidyverse)
library(knitr)
library(kableExtra)
library(boot)
library(parallel)
library(bayestestR)
library(patchwork)
library(mdthemes)
library(lme4)
library(sjPlot)
library(emmeans)
library(ggstance)
library(janitor)
# library(merTools) called via merTools:: to avoid namespace collisions between MASS and dplyr
# set seed for reproducibility
set.seed(42)
# options
options(knitr.table.format = "html") # necessary configuration of tables
# disable scientific notation
options(scipen = 999)
# function to round all numeric vars in a data frame
round_df <- function(df, n_digits = 3) {
require(janitor)
df %>% mutate_if(is.numeric, janitor::round_half_up, digits = n_digits)
}
# create necessary directories
dir.create("../data/processed")
dir.create("../data/results")
#dir.create("models")
# get data
# data with confidence intervals
data_estimates_D <- read_csv("../data/processed/data_estimates_D.csv") %>%
filter(method == "bca")
data_estimates_iat_D <- read_csv("../data/processed/data_estimates_iat_D.csv") %>%
mutate(trial_type = "iat",
unique_id = as.factor(unique_id))
data_demographics_iat <- read_rds("../data/processed/data_iat_processed_participant_level.rds") %>%
mutate(session_id = as.factor(session_id)) %>%
semi_join(data_estimates_iat_D, by = c("session_id" = "unique_id")) %>%
select(unique_id = session_id, age, sex)data_demographics_iat %>%
summarize(min_age = round_half_up(min(age, na.rm = TRUE), 2),
max_age = round_half_up(max(age, na.rm = TRUE), 2),
mean_age = round_half_up(mean(age, na.rm = TRUE), 2),
sd_age = round_half_up(sd(age, na.rm = TRUE), 2)) %>%
gather() %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| key | value |
|---|---|
| min_age | 8.00 |
| max_age | 81.00 |
| mean_age | 31.60 |
| sd_age | 12.46 |
data_demographics_iat %>%
count(sex) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| sex | n |
|---|---|
| f | 6296 |
| m | 3193 |
| NA | 11 |
Most probable estimate among the most probable estimates
data_map_ci_widths <- data_estimates_iat_D %>%
group_by(domain, trial_type) %>%
do(point_estimate(.$ci_width, centrality = "MAP")) %>%
ungroup()
write_csv(data_map_ci_widths, "../data/results/data_map_ci_widths_iat_d.csv")
data_map_ci_widths %>%
summarize(map_map = point_estimate(MAP, centrality = "MAP"),
min_map = min(MAP),
max_map = max(MAP)) %>%
unnest(map_map) %>%
rename(MAP_MAP = MAP) %>%
round_df(2) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| MAP_MAP | min_map | max_map |
|---|---|---|
| 0.71 | 0.47 | 0.72 |
By domain and trial type
data_map_ci_widths %>%
pivot_wider(names_from = trial_type, values_from = MAP) %>%
round_df(2) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| domain | iat |
|---|---|
| 50 Cent - Britney Spears | 0.70 |
| African Americans - European Americans | 0.70 |
| Artists - Musicians | 0.69 |
| Asians - Whites | 0.71 |
| Astrology - Science | 0.70 |
| Atheism - Religion | 0.71 |
| Athletic People - Intelligent People | 0.72 |
| Avoiding - Approaching | 0.51 |
| Bill Clinton - Hillary Clinton | 0.70 |
| Briefs - Boxers | 0.69 |
| Burger King - McDonald’s | 0.70 |
| Canadian - American | 0.71 |
| Capital Punishment - Imprisonment | 0.71 |
| Career - Family | 0.70 |
| Chaos - Order | 0.56 |
| Coffee - Tea | 0.70 |
| Cold - Hot | 0.71 |
| Conservatives - Liberals | 0.68 |
| Corporations - Nonprofits | 0.71 |
| David Letterman - Jay Leno | 0.71 |
| Denzel Washington - Tom Cruise | 0.69 |
| Determinism - Free will | 0.67 |
| Difficult - Simple | 0.66 |
| Dogs - Cats | 0.66 |
| Dramas - Comedies | 0.69 |
| Drinking - Abstaining | 0.71 |
| Effort - Talent | 0.67 |
| Evolution - Creationism | 0.70 |
| Fat People - Thin People | 0.70 |
| Foreign Places - American Places | 0.71 |
| Friends - Family | 0.71 |
| Gay People - Straight People | 0.71 |
| George Bush - John Kerry | 0.71 |
| Gun Control - Gun Rights | 0.71 |
| Helpers - Leaders | 0.70 |
| Hiphop - Classical | 0.71 |
| Innocence - Wisdom | 0.70 |
| Japan - United States | 0.71 |
| Jazz - Teen Pop | 0.70 |
| Jews - Christians | 0.71 |
| Jocks - Nerds | 0.72 |
| Kobe - Shaq | 0.71 |
| Lawyers - Politicians | 0.71 |
| Lord of the Rings - Harry Potter | 0.70 |
| Manufactured - Natural | 0.63 |
| Meat - Vegetables | 0.69 |
| Meg Ryan - Julia Roberts | 0.70 |
| Microsoft - Apple | 0.71 |
| Money - Love | 0.47 |
| Mother Teresa - Princess Diana | 0.71 |
| Mountains - Ocean | 0.71 |
| Muslims - Jews | 0.70 |
| National Defense - Education | 0.69 |
| New York - California | 0.71 |
| Night - Morning | 0.68 |
| Numbers - Letters | 0.70 |
| Old People - Young People | 0.71 |
| Organized Labor - Management | 0.71 |
| Pants - Skirts | 0.71 |
| Past - Future | 0.65 |
| Pepsi - Coke | 0.71 |
| Poor People - Rich People | 0.56 |
| Private - Public | 0.70 |
| Prolife - Prochoice | 0.71 |
| Protein - Carbohydrates | 0.71 |
| Protestants - Catholics | 0.71 |
| Punishment - Forgiveness | 0.57 |
| Realism - Idealism | 0.71 |
| Reason - Emotions | 0.71 |
| Rebellious - Conforming | 0.68 |
| Receiving - Giving | 0.71 |
| Redsox - Yankees | 0.71 |
| Relaxing - Exercising | 0.71 |
| Republicans - Democrats | 0.68 |
| Rich People - Beautiful People | 0.71 |
| Security - Freedom | 0.71 |
| Single - Married | 0.71 |
| Skeptical - Trusting | 0.51 |
| Solitude - Companionship | 0.70 |
| Southerners - Northerners | 0.70 |
| Speed - Accuracy | 0.71 |
| Stable - Flexible | 0.71 |
| State - Church | 0.71 |
| Strong - Sensitive | 0.71 |
| Tall People - Short People | 0.70 |
| Tax Reductions - Social Programs | 0.71 |
| Team - Individual | 0.71 |
| Technology - Nature | 0.69 |
| Television - Books | 0.70 |
| Tradition - Progress | 0.71 |
| Traditional Values - Feminism | 0.71 |
| Urban - Rural | 0.71 |
| West Coast - East Coast | 0.71 |
| Winter - Summer | 0.71 |
| Wrinkles - Plastic Surgery | 0.71 |
data_ci_width_map_D <- data_estimates_iat_D %>%
group_by(domain, trial_type) %>%
do(point_estimate(.$ci_width, centrality = "MAP")) %>%
ungroup() %>%
mutate(MAP = round_half_up(MAP, 3),
trial_type = case_when(trial_type == "tt1" ~ "Trial type 1",
trial_type == "tt2" ~ "Trial type 2",
trial_type == "tt3" ~ "Trial type 3",
trial_type == "tt4" ~ "Trial type 4",
trial_type == "iat" ~ "IAT"),
trial_type = fct_relevel(trial_type, "Trial type 1", "Trial type 2", "Trial type 3", "Trial type 4", "IAT")) %>%
mutate(domain = fct_rev(domain))
# save to disk
write_csv(data_ci_width_map_D, "../data/results/data_ci_width_map_iat_D.csv")
# plot
p_ci_widths <-
ggplot(data_ci_width_map_D, aes(MAP, domain)) +
geom_point(position = position_dodge(width = 0.8)) +
mdthemes::md_theme_linedraw() +
#facet_wrap(~ trial_type, ncol = 4, nrow = 1) +
labs(x = "Highest probability (MAP) 95% CI width",
y = "") +
theme(legend.position = "top")
p_ci_widthsp_cis_by_domain <-
data_estimates_iat_D %>%
arrange(estimate) %>%
group_by(domain) %>%
mutate(ordered_id = row_number()/n()) %>%
ungroup() %>%
ggplot() +
geom_linerange(aes(x = ordered_id, ymin = ci_lower, ymax = ci_upper, color = sig),
alpha = 1) +
geom_point(aes(ordered_id, estimate), size = 0.5, shape = "square") +
geom_hline(yintercept = 0, linetype = "dotted") +
mdthemes::md_theme_linedraw() +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "top") +
scale_color_viridis_d(end = 0.6, direction = -1) +
xlab("Ranked participant") +
ylab("*D* score") +
labs(color = "95% CI excludes zero point") +
facet_wrap(~ domain, ncol = 10)
p_cis_by_domaindata_diff_zero <-
bind_rows(
mutate(data_estimates_D, measure = "IRAP"),
mutate(data_estimates_iat_D, measure = "IAT")
) %>%
mutate(measure = fct_relevel(measure, "IRAP", "IAT"),
domain = as.factor(domain),
trial_type = case_when(trial_type == "tt1" ~ "Trial type 1",
trial_type == "tt2" ~ "Trial type 2",
trial_type == "tt3" ~ "Trial type 3",
trial_type == "tt4" ~ "Trial type 4",
trial_type == "iat" ~ "IAT"),
trial_type = fct_relevel(trial_type, "Trial type 1", "Trial type 2", "Trial type 3", "Trial type 4", "IAT")) %>%
group_by(domain, trial_type, measure) %>%
summarize(proportion_diff_zero = mean(sig),
variance = plotrix::std.error(sig)^2,
.groups = "drop") %>%
# model cannot be run on zero variance or 0 or 1 logit, so offset by a minuscule amount
mutate(proportion_diff_zero_temp = case_when(proportion_diff_zero < 0.001 ~ 0.001,
proportion_diff_zero > 0.999 ~ 0.999,
TRUE ~ proportion_diff_zero),
proportion_diff_zero_logit = boot::logit(proportion_diff_zero_temp)) %>%
select(-proportion_diff_zero_temp) %>%
#filter(!(proportion_diff_zero == 0 & variance == 0)) %>%
mutate(variance = ifelse(variance == 0, 0.0001, variance))
# data_diff_zero %>%
# round_df(2) %>%
# kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)
# save to disk
write_csv(data_diff_zero, "../data/results/data_diff_zero_irap_vs_iat.csv")p_diff_zero <-
data_diff_zero %>%
filter(measure == "IAT") %>%
mutate(domain = fct_rev(factor(domain))) %>%
ggplot(aes(proportion_diff_zero, domain)) +
geom_linerangeh(aes(xmin = proportion_diff_zero - sqrt(variance)*1.96,
xmax = proportion_diff_zero + sqrt(variance)*1.96),
position = position_dodge(width = 0.75)) +
geom_point(position = position_dodge(width = 0.75)) +
#scale_shape_manual(labels = c("IRAP", "IAT"), values = c(15, 16)) +
#scale_color_viridis_d(begin = 0.3, end = 0.7, labels = c("IRAP", "IAT")) +
mdthemes::md_theme_linedraw() +
labs(x = "Proportion of scores<br/>different from zero point",
y = "") +
theme(legend.position = "top",
panel.spacing = unit(1.5, "lines")) +
coord_cartesian(xlim = c(0,1))
p_diff_zeroNB model is slightly different to the one used to compare IRAP D and PI scores: that one has (1) no random slope for measure and (2) a random intercept for trial type too. Including (1) seemed important given that the two IRAP and IAT demonstrated very different heterogeneity between domains. Not including it greatly and inappropriately expands the prediction intervals on the IRAP (i.e., variation in the IAT is modeled as variation in both, in appropriately). In contrast, the effects were very similar between IRAP D and IRAP PI, so this wasn’t necessary in the other analysis. Including (2) gave convergence issues, likely because the IAT only has a single trial type, so it was dropped.
# fit model
fit_diff_zero <-
lmer(proportion_diff_zero_logit ~ 1 + measure + (measure | domain),
weights = 1/variance,
data = data_diff_zero,
# solution from https://www.metafor-project.org/doku.php/tips:rma_vs_lm_lme_lmer
control = lmerControl(check.nobs.vs.nlev = "ignore",
check.nobs.vs.nRE = "ignore"))
# extract marginal means
results_emm_diff_zero <-
summary(emmeans(fit_diff_zero, ~ measure)) %>%
dplyr::select(measure, estimate = emmean, se = SE, ci_lower = lower.CL, ci_upper = upper.CL)
# extract re Tau
results_re_tau_diff_zero <- fit_diff_zero %>%
merTools::REsdExtract() %>%
as_tibble(rownames = "re") %>%
rename(tau = value)
# combine
results_diff_zero <- results_emm_diff_zero %>%
mutate(pi_lower = estimate - (1.96 * sqrt(se^2 + results_re_tau_diff_zero$tau^2)), # as in metafor package's implementation of prediction intervals, see metafor::predict.rma.R
pi_upper = estimate + (1.96 * sqrt(se^2 + results_re_tau_diff_zero$tau^2))) |>
select(-se) |>
mutate_if(is.numeric, boot::inv.logit)
# plot
p_prop_nonzero <-
ggplot(results_diff_zero, aes(measure, estimate)) +
geom_linerange(aes(ymin = pi_lower, ymax = pi_upper), size = 0.5, position = position_dodge(width = 0.8), linetype = "dotted") +
geom_linerange(aes(ymin = ci_lower, ymax = ci_upper), size = 1.25, position = position_dodge(width = 0.8)) +
geom_point(position = position_dodge(width = 0.8), size = 2.5) +
mdthemes::md_theme_linedraw() +
scale_y_continuous(breaks = c(0, .25, .5, .75, 1), labels = c("0.00<br/>(Worse)", "0.25", "0.50", "0.75", "1.00<br/>(Better)")) +
#scale_color_viridis_d(alpha = 1, begin = 0.3, end = 0.7, labels = c("IRAP", "IAT")) +
#scale_shape_manual(labels = c("IRAP", "IAT"), values = c(15, 16)) +
scale_x_discrete(labels = c("IRAP D scores", "IAT D scores")) +
labs(x = "",
y = "Proportion of scores<br/>different from zero point<br/>") +
theme(legend.position = "none") +
coord_flip(ylim = c(0, 1))
p_prop_nonzeroresults_diff_zero %>%
round_df(2) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| measure | estimate | ci_lower | ci_upper | pi_lower | pi_upper |
|---|---|---|---|---|---|
| IRAP | 0.06 | 0.04 | 0.10 | 0.00 | 0.62 |
| IAT | 0.57 | 0.52 | 0.62 | 0.06 | 0.97 |
# tests
data_emms_diff_zero <- emmeans(fit_diff_zero, list(pairwise ~ measure), adjust = "holm")
summary(data_emms_diff_zero)$`pairwise differences of measure` %>%
as.data.frame() %>%
select(comparison = 1, p.value) %>%
mutate(p.value = ifelse(p.value < .001, "< .001", round_half_up(p.value, 3))) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| comparison | p.value |
|---|---|
| IRAP - IAT | < .001 |
Within domain and trial type.
Note: Discriminability between a score and zero can be determined using the CI, because zero is a known value and only the score is measured with uncertainty. However, discriminability between two scores must take into account the uncertainty in the estimation of both scores. Weir (2005) argues that such an interval can be estimated by expanding the CIs by sqrt(2). Here I refer to these intervals as Discriminability Intervals (DIs).
# # discriminability using non-overlap of CIs
# discriminability <- function(data, i) {
# data_with_indexes <- data[i,] # boot function requires data and index
# ci_lower <- data_with_indexes$ci_lower
# ci_upper <- data_with_indexes$ci_upper
# n_ci_lower <- length(ci_lower)
# n_ci_upper <- length(ci_upper)
# r_ci_lower <- sum(rank(c(ci_lower, ci_upper))[1:n_ci_lower])
# A <- (r_ci_lower / n_ci_lower - (n_ci_lower + 1) / 2) / n_ci_upper
# return(A)
# }
# discriminatory using the significance of the difference score
# the goal here is to assess mean_diff > 1.96 * sqrt(SE1^2 + SE2^2 for every possible comparison EXCLUDING self comparisons. This is tricky to do within a typical tidyverse workflow as it means doing mutates involving each row of a column and every other row of that column but not the same row.
# the below solution is to use expand.grid to find all combinations of a row with itself, and then use the modulus of the length of the row to filter out the self-pairings. Then do mutates on the rows to assess significant differences. It's enough to then summarize the proportion of significant results across all participants.
discriminability <- function(data, i) {
data_with_indexes <- data[i,] # boot function requires data and index
grid_estimates <- expand.grid(data_with_indexes$estimate, data_with_indexes$estimate) |>
mutate(diff = Var1 - Var2,
row_number = row_number(),
modulus = row_number %% (nrow(data_with_indexes)+1)) |>
filter(modulus != 1) |>
select(diff)
grid_se <- expand.grid(data_with_indexes$se, data_with_indexes$se) |>
mutate(critical_value = 1.96 * sqrt(Var1^2 + Var2^2),
row_number = row_number(),
modulus = row_number %% (nrow(data_with_indexes)+1)) |>
filter(modulus != 1) |>
select(critical_value)
proportion_sig_diff <-
bind_cols(grid_estimates, grid_se) |>
mutate(sig = abs(diff) > critical_value) |>
summarize(proportion_sig_diff = mean(sig)) |>
pull(proportion_sig_diff)
return(proportion_sig_diff)
}
bootstrap_discriminability <- function(data){
require(dplyr)
require(boot)
fit <-
boot::boot(data = data,
statistic = discriminability,
R = 2000,
sim = "ordinary",
stype = "i",
parallel = "multicore",
ncpus = parallel::detectCores()-1)
results <- boot::boot.ci(fit, conf = 0.95, type = "bca")
output <-
tibble(
estimate = fit$t0,
ci_lower = results$bca[4],
ci_upper = results$bca[5]
)
return(output)
}
# irap data
data_discriminability_D <- read_csv("../data/results/data_discriminability_D.csv") %>%
filter(method == "bca")
# bootstrapping has a long execution time, so load saved values if they've already been calculated
if(file.exists("../data/results/data_discriminability_iat_D.csv")) {
data_discriminability_iat_D <- read_csv("../data/results/data_discriminability_iat_D.csv")
} else {
# bootstrap D scores
data_discriminability_iat_D <- data_estimates_iat_D |>
mutate(se = (ci_upper - ci_lower)/(1.96*2)) |>
select(unique_id, domain, trial_type, estimate, se) |>
group_by(domain, trial_type) |>
do(bootstrap_discriminability(data = .)) |>
ungroup() |>
rename(proportion_discriminable = estimate) |>
mutate(variance = (((ci_upper - ci_lower)/(1.96*2)))^2,
domain = as.factor(domain),
#trial_type = fct_relevel(trial_type, "tt1", "tt2", "tt3", "tt4", "iat"),
measure = "IAT")
# save to disk
write_csv(data_discriminability_iat_D, "../data/results/data_discriminability_iat_D.csv")
}# combine
data_discriminability_combined <-
bind_rows(
mutate(data_discriminability_D, measure = "IRAP"),
mutate(data_discriminability_iat_D, measure = "IAT")
) %>%
mutate(measure = fct_relevel(measure, "IRAP", "IAT"),
trial_type = case_when(trial_type == "tt1" ~ "Trial type 1",
trial_type == "tt2" ~ "Trial type 2",
trial_type == "tt3" ~ "Trial type 3",
trial_type == "tt4" ~ "Trial type 4",
trial_type == "iat" ~ "IAT"),
trial_type = fct_relevel(trial_type, "Trial type 1", "Trial type 2", "Trial type 3", "Trial type 4", "IAT")) %>%
#filter(!(proportion_discriminable == 0 & variance == 0)) %>%
mutate(variance = ifelse(variance == 0, 0.0001, variance)) |>
# model cannot be run on zero variance or 0 or 1 logit, so offset by a minuscule amount
mutate(
proportion_discriminable_temp = case_when(proportion_discriminable < 0.001 ~ 0.001,
proportion_discriminable > 0.999 ~ 0.999,
TRUE ~ proportion_discriminable),
proportion_discriminable_logit = boot::logit(proportion_discriminable_temp)
) %>%
select(-proportion_discriminable_temp)
p_discriminability <-
data_discriminability_combined %>%
filter(measure == "IAT") %>%
mutate(domain = fct_rev(factor(domain))) %>%
ggplot(aes(proportion_discriminable, domain)) +
geom_linerangeh(aes(xmin = proportion_discriminable - sqrt(variance)*1.96,
xmax = proportion_discriminable + sqrt(variance)*1.96),
position = position_dodge(width = 0.75)) +
geom_point(position = position_dodge(width = 0.75)) +
#scale_shape_manual(labels = c("IRAP", "IAT"), values = c(15, 16)) +
#scale_color_viridis_d(begin = 0.3, end = 0.7, labels = c("IRAP", "IAT")) +
mdthemes::md_theme_linedraw() +
#facet_wrap(~ trial_type, ncol = 4) +
labs(x = "Proportion of scores<br/>differerent from other scores",
y = "") +
theme(legend.position = "top",
panel.spacing = unit(1.5, "lines")) +
coord_cartesian(xlim = c(0,1))
p_discriminability# fit meta analytic model
fit_disciminability <-
lmer(proportion_discriminable_logit ~ 1 + measure + (measure | domain),
weights = 1/variance,
data = data_discriminability_combined,
# solution from https://www.metafor-project.org/doku.php/tips:rma_vs_lm_lme_lmer
control = lmerControl(check.nobs.vs.nlev = "ignore",
check.nobs.vs.nRE = "ignore"))
# extract marginal means
results_emm_disciminability <-
summary(emmeans(fit_disciminability, ~ measure)) %>%
dplyr::select(measure, estimate = emmean, se = SE, ci_lower = lower.CL, ci_upper = upper.CL)
# extract re Tau
results_re_tau_disciminability <- fit_disciminability %>%
merTools::REsdExtract() %>%
as_tibble(rownames = "re") %>%
rename(tau = value)
# combine
results_disciminability <- results_emm_disciminability %>%
mutate(pi_lower = estimate - (1.96 * sqrt(se^2 + results_re_tau_disciminability$tau^2)), # as in metafor package's implementation of credibility intervals, see metafor::predict.rma.R
pi_upper = estimate + (1.96 * sqrt(se^2 + results_re_tau_disciminability$tau^2))) |>
select(-se) |>
mutate_if(is.numeric, boot::inv.logit)
# plot
p_prop_discriminable <-
ggplot(results_disciminability, aes(measure, estimate)) +
geom_linerange(aes(ymin = pi_lower, ymax = pi_upper), size = 0.5, position = position_dodge(width = 0.8), linetype = "dotted") +
geom_linerange(aes(ymin = ci_lower, ymax = ci_upper), size = 1.25, position = position_dodge(width = 0.8)) +
geom_point(position = position_dodge(width = 0.8), size = 2.5) +
scale_y_continuous(breaks = c(0, .25, .5, .75, 1), labels = c("0.00<br/>(Worse)", "0.25", "0.50", "0.75", "1.00<br/>(Better)")) +
#scale_shape_manual(labels = c("IRAP", "IAT"), values = c(15, 16)) +
#scale_color_viridis_d(begin = 0.3, end = 0.7, labels = c("IRAP", "IAT")) +
scale_x_discrete(labels = c("IRAP D scores", "IAT D scores")) +
mdthemes::md_theme_linedraw() +
labs(x = "",
y = "Proportion of scores<br/>differerent from other scores<br/>") +
theme(legend.position = "none") +
coord_flip(ylim = c(0, 1))
p_prop_discriminable results_disciminability %>%
round_df(2) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| measure | estimate | ci_lower | ci_upper | pi_lower | pi_upper |
|---|---|---|---|---|---|
| IRAP | 0.07 | 0.05 | 0.10 | 0.01 | 0.47 |
| IAT | 0.46 | 0.42 | 0.49 | 0.07 | 0.91 |
# tests
data_emms_disciminability <- emmeans(fit_disciminability, list(pairwise ~ measure), adjust = "holm")
summary(data_emms_disciminability)$`pairwise differences of measure` %>%
as.data.frame() %>%
select(comparison = 1, p.value) %>%
mutate(p.value = ifelse(p.value < .001, "< .001", round_half_up(p.value, 3))) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| comparison | p.value |
|---|---|
| IRAP - IAT | < .001 |
NB observed range of confidence intervals
## calculate observed ranges
observed_range_estimates_D <- data_estimates_D %>%
group_by(domain, trial_type) %>%
dplyr::summarize(min = min(ci_lower, na.rm = TRUE),
max = max(ci_upper, na.rm = TRUE),
.groups = "drop") %>%
mutate(range = max - min)
observed_range_estimates_iat_D <- data_estimates_iat_D %>%
group_by(domain) %>%
dplyr::summarize(min = min(ci_lower, na.rm = TRUE),
max = max(ci_upper, na.rm = TRUE),
.groups = "drop") %>%
mutate(range = max - min)
# calculate CI / range
data_ci_width_proportions_D <- data_estimates_D %>%
# join this data into the original data
full_join(observed_range_estimates_D, by = c("domain", "trial_type")) %>%
# calculate ci width as a proportion of observed range
mutate(ci_width_proportion = ci_width / range) %>%
mutate(measure = "IRAP")
data_ci_width_proportions_iat_D <- data_estimates_iat_D %>%
# join this data into the original data
full_join(observed_range_estimates_iat_D, by = "domain") %>%
# calculate ci width as a proportion of observed range
mutate(ci_width_proportion = ci_width / range) %>%
mutate(measure = "IAT")
# combine
data_ci_width_proportions_combined <-
bind_rows(
data_ci_width_proportions_D,
data_ci_width_proportions_iat_D
) %>%
mutate(measure = fct_relevel(measure, "IRAP", "IAT"),
domain = as.factor(domain),
trial_type = fct_relevel(trial_type, "tt1", "tt2", "tt3", "tt4", "iat")) %>%
group_by(measure, domain, trial_type) %>%
summarize(ci_width_proportion_mean = mean(ci_width_proportion),
variance = plotrix::std.error(ci_width_proportion)^2) %>%
ungroup() %>%
# logit transform
mutate(ci_width_proportion_mean_temp = case_when(ci_width_proportion_mean < 0.0001 ~ 0.0001,
ci_width_proportion_mean > 0.9999 ~ 0.9999,
TRUE ~ ci_width_proportion_mean),
ci_width_proportion_mean_logit = boot::logit(ci_width_proportion_mean_temp)) %>%
select(-ci_width_proportion_mean_temp)
write_csv(data_ci_width_proportions_combined, "../data/results/data_ci_width_proportions_irap_d_vs_iat_d.csv")p_coverage <-
data_ci_width_proportions_combined %>%
mutate(domain = fct_rev(factor(domain))) %>%
filter(measure == "IAT") %>%
ggplot(aes(ci_width_proportion_mean, domain)) +
geom_point(position = position_dodge(width = 0.75)) +
scale_shape_manual(labels = c("*D* scores", "PI scores"), values = c(15, 16)) +
geom_linerangeh(aes(xmin = ci_width_proportion_mean - sqrt(variance)*1.96,
xmax = ci_width_proportion_mean + sqrt(variance)*1.96),
position = position_dodge(width = 0.75)) +
scale_color_viridis_d(begin = 0.3, end = 0.7, labels = c("*D* scores", "PI scores")) +
mdthemes::md_theme_linedraw() +
#facet_wrap(~ trial_type, ncol = 4) +
labs(x = "Proportion of observed range covered<br/>by individual scores' 95% CIs",
y = "") +
theme(legend.position = "top",
panel.spacing = unit(1.5, "lines")) +
coord_cartesian(xlim = c(0,1))
p_coverage# fit model
fit_ci_width_proportions <-
lmer(ci_width_proportion_mean_logit ~ 1 + measure + (measure | domain),
weights = 1/variance,
data = data_ci_width_proportions_combined,
# solution from https://www.metafor-project.org/doku.php/tips:rma_vs_lm_lme_lmer
control = lmerControl(check.nobs.vs.nlev = "ignore",
check.nobs.vs.nRE = "ignore"))
# extract marginal means
results_emm_ci_width_proportions <-
summary(emmeans(fit_ci_width_proportions, ~ measure)) %>%
dplyr::select(measure, estimate = emmean, se = SE, ci_lower = lower.CL, ci_upper = upper.CL)
# extract re Tau
results_re_tau_ci_width_proportions <-
merTools::REsdExtract(fit_ci_width_proportions) %>%
as_tibble(rownames = "re") %>%
rename(tau = value)
# combine
results_ci_width_proportions <- results_emm_ci_width_proportions %>%
mutate(pi_lower = estimate - (1.96 * sqrt(se^2 + results_re_tau_ci_width_proportions$tau^2)), # as in metafor package's implementation of credibility intervals, see metafor::predict.rma.R
pi_upper = estimate + (1.96 * sqrt(se^2 + results_re_tau_ci_width_proportions$tau^2))) %>%
select(-se) %>%
mutate_if(is.numeric, boot::inv.logit)
# plot
p_ci_width_proportion_observed_range <-
ggplot(results_ci_width_proportions, aes(measure, estimate,
)) +
geom_linerange(aes(ymin = pi_lower, ymax = pi_upper), size = 0.5, position = position_dodge(width = 0.8), linetype = "dotted") +
geom_linerange(aes(ymin = ci_lower, ymax = ci_upper), size = 1.25, position = position_dodge(width = 0.8)) +
geom_point(position = position_dodge(width = 0.8), size = 2.5) +
#scale_shape_discrete(labels = c("IRAP", "IAT")) +
scale_y_continuous(breaks = c(0, .25, .5, .75, 1), labels = c("0.00<br/>(Better)", "0.25", "0.50", "0.75", "1.00<br/>(Worse)")) +
#scale_shape_manual(labels = c("IRAP", "IAT"), values = c(15, 16)) +
#scale_color_viridis_d(begin = 0.3, end = 0.7, labels = c("IRAP", "IAT")) +
scale_x_discrete(labels = c("IRAP D scores", "IAT D scores")) +
mdthemes::md_theme_linedraw() +
labs(x = "",
y = "Proportion of observed range covered<br/>by individual scores' 95% CIs") +
theme(legend.position = "none") +
coord_flip(ylim = c(0, 1))
p_ci_width_proportion_observed_rangeresults_ci_width_proportions %>%
round_df(2) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| measure | estimate | ci_lower | ci_upper | pi_lower | pi_upper |
|---|---|---|---|---|---|
| IRAP | 0.51 | 0.49 | 0.53 | 0.42 | 0.60 |
| IAT | 0.27 | 0.26 | 0.27 | 0.16 | 0.41 |
# tests
data_emms_ci_width_proportions <- emmeans(fit_ci_width_proportions, list(pairwise ~ measure), adjust = "holm")
summary(data_emms_ci_width_proportions)$`pairwise differences of measure` %>%
as.data.frame() %>%
select(comparison = 1, p.value) %>%
mutate(p.value = ifelse(p.value < .001, "< .001", round_half_up(p.value, 3))) %>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)| comparison | p.value |
|---|---|
| IRAP - IAT | < .001 |
p_cis_by_domainggsave(filename = "plots/supplementary_figure_2S_cis_by_domain_iat_d.pdf",
plot = p_cis_by_domain,
device = "pdf",
# path = NULL,
# dpi = 300,
units = "in",
width = 16,
height = 16,
limitsize = TRUE)p_ci_widthsggsave(filename = "plots/supplementary_figure_3S_ci_widths_iat_d.pdf",
plot = p_ci_widths,
device = "pdf",
# path = NULL,
# dpi = 300,
units = "in",
width = 6,
height = 12,
limitsize = TRUE)p_diff_zeroggsave(filename = "plots/supplementary_figure_4S_proportion_excluding_zero_point_iat.pdf",
plot = p_diff_zero,
device = "pdf",
# path = NULL,
# dpi = 300,
units = "in",
width = 6,
height = 14,
limitsize = TRUE)p_discriminabilityggsave(filename = "plots/supplementary_figure_5S_proportion_discriminable_iat.pdf",
plot = p_discriminability,
device = "pdf",
# path = NULL,
# dpi = 300,
units = "in",
width = 6,
height = 14,
limitsize = TRUE)p_coverageggsave(filename = "plots/supplementary_figure_6S_proportion_coverage_iat.pdf",
plot = p_coverage,
device = "pdf",
# path = NULL,
# dpi = 300,
units = "in",
width = 6,
height = 14,
limitsize = TRUE)p_combined <-
p_prop_nonzero +
p_prop_discriminable +
p_ci_width_proportion_observed_range +
plot_layout(ncol = 1) #, guides = "collect") & theme(legend.position = "top")
p_combinedggsave(filename = "plots/figure_6_metaanalyses_irap_d_vs_iat_d.pdf",
plot = p_combined,
device = "pdf",
# path = NULL,
# dpi = 300,
units = "in",
width = 5,
height = 5,
limitsize = TRUE)sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] janitor_2.1.0 ggstance_0.3.5 emmeans_1.7.5 sjPlot_2.8.10
## [5] lme4_1.1-30 Matrix_1.4-1 mdthemes_0.1.0 patchwork_1.1.1
## [9] bayestestR_0.12.1 boot_1.3-28 kableExtra_1.3.4 knitr_1.39
## [13] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
## [17] readr_2.1.2 tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6
## [21] tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.1-1 googledrive_2.0.0 minqa_1.2.4
## [4] colorspace_2.0-3 ellipsis_0.3.2 sjlabelled_1.2.0
## [7] estimability_1.4 snakecase_0.11.0 markdown_1.1
## [10] parameters_0.18.1 fs_1.5.2 gridtext_0.1.4
## [13] ggtext_0.1.1 rstudioapi_0.13 listenv_0.8.0
## [16] furrr_0.3.0 farver_2.1.1 bit64_4.0.5
## [19] fansi_1.0.3 mvtnorm_1.1-3 lubridate_1.8.0
## [22] xml2_1.3.3 codetools_0.2-18 splines_4.2.1
## [25] cachem_1.0.6 sjmisc_2.8.9 jsonlite_1.8.0
## [28] nloptr_2.0.3 ggeffects_1.1.2 pbkrtest_0.5.1
## [31] broom_1.0.0 dbplyr_2.2.1 broom.mixed_0.2.9.4
## [34] shiny_1.7.2 effectsize_0.7.0 compiler_4.2.1
## [37] httr_1.4.3 sjstats_0.18.1 backports_1.4.1
## [40] assertthat_0.2.1 fastmap_1.1.0 gargle_1.2.0
## [43] cli_3.3.0 later_1.3.0 htmltools_0.5.3
## [46] tools_4.2.1 coda_0.19-4 gtable_0.3.0
## [49] glue_1.6.2 merTools_0.5.2 Rcpp_1.0.9
## [52] cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.4.1
## [55] svglite_2.1.0 nlme_3.1-157 iterators_1.0.14
## [58] insight_0.18.0 xfun_0.31 globals_0.15.1
## [61] rvest_1.0.2 mime_0.12 lifecycle_1.0.1
## [64] googlesheets4_1.0.0 future_1.27.0 MASS_7.3-57
## [67] zoo_1.8-10 scales_1.2.0 vroom_1.5.7
## [70] promises_1.2.0.1 hms_1.1.1 sandwich_3.0-2
## [73] yaml_2.3.5 sass_0.4.2 stringi_1.7.8
## [76] highr_0.9 foreach_1.5.2 plotrix_3.8-2
## [79] blme_1.0-5 rlang_1.0.4 pkgconfig_2.0.3
## [82] systemfonts_1.0.4 arm_1.12-2 evaluate_0.15
## [85] lattice_0.20-45 labeling_0.4.2 bit_4.0.4
## [88] tidyselect_1.1.2 parallelly_1.32.1 magrittr_2.0.3
## [91] R6_2.5.1 generics_0.1.3 multcomp_1.4-20
## [94] DBI_1.1.3 pillar_1.8.0 haven_2.5.0
## [97] withr_2.5.0 abind_1.4-5 survival_3.3-1
## [100] datawizard_0.4.1 performance_0.9.1 modelr_0.1.8
## [103] crayon_1.5.1 utf8_1.2.2 tzdb_0.3.0
## [106] rmarkdown_2.14 grid_4.2.1 readxl_1.4.0
## [109] reprex_2.0.1 digest_0.6.29 webshot_0.5.3
## [112] xtable_1.8-4 httpuv_1.6.5 munsell_0.5.0
## [115] viridisLite_0.4.0 bslib_0.4.0